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Conference Paper

#### Transductive Inference for Estimating Values of Functions

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##### Citation

Chapelle, O., Vapnik, V., & Weston, J. (2000). Transductive Inference for Estimating
Values of Functions. In S. Solla, T. Leen, & K. Müller (*Advances
in Neural Information Processing Systems 12* (pp. 421-427). Cambridge, MA, USA: MIT Press.

Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E4CE-8

##### Abstract

We introduce an algorithm for estimating the values of a
function at a set of test points x_1^*,dots,x^*_m given a set of training points (x_1,y_1),dots,(x_ell,y_ell) without estimating (as an intermediate step) the regression function. We demonstrate that this direct (transductive) way for estimating values of the regression (or classification in pattern recognition) is more accurate than the traditional one
based on two steps, first estimating the function and then
calculating the values of this function at the points of interest.